E-Quarantine: Remote Real Time Monitoring of COVID-19 Patients

Abstract

It’s been more than a year since the world is struggling with the COVID-19 pandemic. Mutation of the virus leads to a new wave of infection in a lot of countries. The virus has a very high spreading rate, so all the infected patients won’t be able to treat in the hospitals and chances of it spreading among healthcare workers is also high. So we propose a system to monitor COVID-19 patients undergoing quarantine from their own homes during the pandemic, so as to save the hospital bed spaces for the patients with a critical health condition, who need immediate medical attention. The proposed system helps us to avoid overcrowding in hospitals and thereby avoiding the spreading of the virus from highly infected patients to the unaffected individuals. The methodology utilizes LSTM model which is a recurrent neural network (RNN) architecture used in the field of deep learning.